M2DA: ドライバー注意を取り入れた自動運転向けマルチモーダル融合トランスフォーマ
M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving
LiDARと視覚の融合モジュールとドライバー注意を組み合わせ、複雑な交通状況で危険な対象を人間のように捉える自動運転モデルを提案し、CARLAで最先端性能を達成した。
著者: Dongyang Xu, Haokun Li, Qingfan Wang, Ziying Song, Lei Chen, Hanming Deng
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
End-to-end autonomous driving has witnessed remarkable progress. However, the extensive deployment of autonomous vehicles has yet to be realized, primarily due to 1) inefficient multi-modal environment perception: how to integrate data from multi-modal sensors more efficiently; 2) non-human-like scene understanding: how to effectively locate and predict critical risky agents in traffic scenarios like an experienced driver. To overcome these challenges, in this paper, we propose a Multi-Modal fusion transformer incorporating Driver Attention (M2DA) for autonomous driving. To better fuse multi-modal data and achieve higher alignment between different modalities, a novel Lidar-Vision-Attention-based Fusion (LVAFusion) module is proposed. By incorporating driver attention, we empower the human-like scene understanding ability to autonomous vehicles to identify crucial areas within complex scenarios precisely and ensure safety. We conduct experiments on the CARLA simulator and achieve state-of-the-art performance with less data in closed-loop benchmarks. Source codes are available at https://anonymous.4open.science/r/M2DA-4772.
